Machine Learning in Human Resource

Machine learning has been widely utilized by various industries such as oil and gas, retail, banking, and cyber-security.

Machine learning in human resources has been slowly gaining adoption, yet it is recognized as a game-changing technology. Similar to Amazon’s Alexa transforming people’s lives at home, AI and machine learning have also evolved into intelligent assistants to help professionals work smarter.

It has been evident that people have very high expectations of new technologies, making it essential to keep up with the new trends and developments with respect to the application of machine learning in human resources.

While machine learning is very good at detecting subtle patterns over time, with a limited number of variables – it is also a versatile tool.

Machine learning is capable of analyzing many different sources of data to find relationships that would otherwise be impossible for an individual to uncover through experience or simple spreadsheet analysis.

An emerging area where this is being applied is within the HR industry which is using it to predict employee outcomes.

Hiring Talent

As we all know, the HR department has to go through hundreds or thousands of resumes, not only to select the right candidates but also to review those resumes. After doing all this though, many candidates don’t turn out to be the right ones for them due to different reasons.

However, machine learning can analyze all this data and find patterns among them that are nearly impossible for humans or current software (which work on fixed parameter than past experiences) to find out.

We can feed data (such as resumes, social media activity, and interview process data) to machines which not only will analyze and find the pattern in the data but also will continuously keep learning from past experiences which will increase the success rate over the period of time.

Employee Attrition

Employee turnover is never good for business. When you think about your investment in recruiting and training employees and only having them stay on for a short period of time, you are not getting back a return on your investment.

Machine learning can be put to use in identifying early warning signs of employee attrition by monitoring employee satisfaction survey results, drops in efficiency, and absenteeism.

Employee Engagement

When you have talented employees, you need to find ways that you can help them expand their skill set. If you don’t engage with them, they will get bored and complacent, and think that they are not growing within the organization.

Employee engagement will always be a human-to-human practice; there can be no doubt about that.

However, there is plenty to be gained from the smart use of machine learning in human resource and software that helps identify trends when it comes to engaging employees by understanding what it is that keeps them happy at their organization.

This can be done by having data from a common platform ingested into an ML system that understands and provides numerous ways to have engagement campaigns driven.

Solutions have already been developed by companies like  Workometry and Glint that are in use by a number of top companies.

These software systems measure, analyze, and report on employee engagement and general feelings related to their work.

Data is collected from a range of sources, many of which were not easy to extract any meaningful information from in the past.

Future of Machine Learning in Human Resource

The human element of HR will never disappear but machine learning in human resource can guide and assist to ensure the functions of these departments are streamlined and faster while strategic and day-to-day decisions will be more accurate.

It will allow HR professionals to begin to make data-driven decisions and deliver innovation in the areas of recruitment, organizational design, workforce management, and employee engagement.

Next Step

Contact us if you need any help with ML/AI for software solutions.

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Dnyaneshwer Pendurkar
Dnyaneshwer Pendurkar
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